Treatment often entails the surgical removal of the canine-especially in cases of transmigration. The findings emphasise the importance of early diagnosis and CT/CBCT imaging for further diagnostics and future research of impacted mandibular canines.
This article takes a global view of risk-based capital management in the insurance business, which nowadays is a key issue of the management of insurance companies. A thorough analysis of recent regulatory authorities' and rating agencies' positions shows that it is also a core topic in discussions about future financial frameworks in general. As a matter of fact, many parties are involved in the development of these frameworks, so there is an urgent need for common principles. This article identifies the essentials of future financial frameworks, highlights aspects which should be affected by a unified economic view, and outlines what progress has already been made.
In this work a co-evolutionary approach is used in conjunction with Genetic Programming operators in order to find certain transition rules for two-step discrete dynamical systems. This issue is similar to the well-known artificial-ant problem. We seek the dynamic system to produce a trajectory leading from given initial values to a maximum of a given spatial functional.This problem is recast into the framework of input-output relations for controllers, and the optimization is performed on program trees describing input filters and finite state machines incorporated by these controllers simultaneously. In the context of Genetic Programming there is always a set of test cases which has to be maintained for the evaluation of program trees. These test cases are subject to evolution here, too, so we employ a so-called host-parasitoid model in order to evolve optimizing dynamical systems.Reinterpreting these systems as algorithms for finding the maximum of a functional under constraints, we have derived a paradigm for the automatic generation of adapted optimization algorithms via optimal control. We provide numerical examples generated by the GP-system MathEvEco. These examples refer to key properties of the resulting strategies and they include statistical evidence showing that for this problem of system identification the co-evolutionary approach is superior to standard Genetic Programming.
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